1. Field of the Invention
The present invention generally relates to collecting data generated by equipment used for processing substrates used in semiconductor, data storage, and allied industries. More particularly, the present invention relates to electronically collecting data from a Semiconductor Equipment Communications Standard (SECS) data port and electronically sniffing data packets from various pieces of equipment in a semiconductor, data storage, or allied industry fabrication (“fab”) facility.
2. Description of the Background Art
The semiconductor, data storage, and allied industries feature manufacturing lines that are rich in data production. Most pieces of process or metrology equipment (“tools”) have a myriad of both generated and stored data. The data may include process conditions within a tool (e.g., process duration, process temperature, process gas flows, etc.), operating conditions of the tool (e.g., alarm states, input/output (I/O) signal traces, vacuum and pressure levels, etc.), or general historical data for the tool (e.g., last preventive maintenance (PM), next PM date, overall uptime, etc.). Tool data may also include various substrate feature measurements such as film thickness mapping, resistivity mapping, particle mapping, die-to-database correlations, step height values, line-width measurements, and so on. Data are typically available from both in-situ and ex-situ tools.
In the semiconductor industry, a fab tool is a piece of semiconductor fabrication equipment designed to process wafers (e.g., an ion implanter, a photolithographic stepper, a chemical vapor deposition system, etc.) or a piece of inspection equipment designed to measure or inspect wafers (e.g., a scanning surface inspection system, a critical-dimension scanning electron microscope, a spectroscopic ellipsometer, etc.). Frequently, a specialized fab tool, called a cluster tool, is also used in advanced fabrication facilities. A cluster tool is an integrated, environmentally isolated tool consisting of process, transport, and cassette modules mechanically linked together. A cluster tool module is an element of a cluster tool that performs particular functions, typically dedicated to a given process or portion of a process. A cluster tool module may also contain other modules.
Typically, there is at least one communication interface used to access data from various tools, the Semiconductor Equipment Communications Standard (SECS) protocol. SECS in one of many international consensus-based standards produced by SEMI (Semiconductor Equipment and Materials International), headquartered in San Jose, Calif. The SECS protocol communicates actively via a tool's serial port. Data available from a SECS protocol provides tool and material information such as wafer information, wafer lot number, cassette slot number being processed, recipe name, and process parameters.
A process or line engineer can use or analyze data from a tool to evaluate processing trends, view run-rules for a given process, or perform complex statistical calculations. However, to date, this type of analysis has been limited to a particular tool or a limited set of data available from a plurality of tools.
Statistical techniques are a type of data analysis for controlling a process that is well known in the semiconductor, data storage, and allied industries. For example, statistical process control has been defined as “the use of statistical methods to analyze a process or its output to take appropriate actions to achieve and maintain a state of statistical control and continuously improve the process capability” (SEMATECH Dictionary,). Appropriate actions may include monitoring or changing gas flows, temperatures, ramp rates, or the like. Other statistical techniques include using Shewhart charts (e.g., charting a group mean versus standard deviation) for evaluating run-rules according to the well-known Western Electric sensitizing rules published in 1956.
However, beyond the basic data provided via a SECS port, the ability to collect real-time data from tools has been limited. Many tools today are connected to a local area network (LAN) but currently available methods of extracting data from a tool over a LAN involve installing custom or proprietary software on the tool, sending data requests to a processor controlling the tool, waiting for acknowledgment of data requests from the tool's processor, preparing the data for spooling or streaming, and then collecting the data. This data collection scheme adds computational and timing overhead to the tool's processor in addition to added complexities and expenditures due to the custom or proprietary software.
What is needed in the art is an improved means to obtain more and/or better data from a plurality of tools without affecting existing tool or fab installations.